Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis is a research paper published in Genome Research (2020). On theSindex it has a DataRank of 0.697. It has been cited 103 times.
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Base Score Contribution
0.697
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institutes of Health
Grant: HG002585
NIH
Grant: GM122930
NHGRI NIH HHS
Grant: R01 HG002585
NIGMS NIH HHS
Grant: R35 GM131726
NIGMS NIH HHS
Grant: R01 GM122930
National Institutes of Health
Grant: 1R01GM122930-01
Using single cell RNA-seq to study regulatory noise and robustness
National Institutes of Health
Grant: 2R01HG002585-09A1
Genome Analysis: Data Accuracy, Haplotyping and Mapping
Fields of Study
MeSH Terms
Keywords
Additional file 1 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data
Additional file 1 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data
Additional file 5 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data
Additional file 5 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data
Additional file 1 of Universal prediction of cell-cycle position using transfer learning
Additional file 1 of Universal prediction of cell-cycle position using transfer learning
Additional file 2 of Universal prediction of cell-cycle position using transfer learning
Additional file 2 of Universal prediction of cell-cycle position using transfer learning
Additional file 2 of Combined exome and transcriptome sequencing of non-muscle-invasive bladder cancer: associations between genomic changes, expression subtypes, and clinical outcomes
Additional file 2 of Combined exome and transcriptome sequencing of non-muscle-invasive bladder cancer: associations between genomic changes, expression subtypes, and clinical outcomes
Additional file 3 of Combined exome and transcriptome sequencing of non-muscle-invasive bladder cancer: associations between genomic changes, expression subtypes, and clinical outcomes
Additional file 3 of Combined exome and transcriptome sequencing of non-muscle-invasive bladder cancer: associations between genomic changes, expression subtypes, and clinical outcomes
Additional file 1 of Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation
Additional file 1 of Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation
Additional file 1 of An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs
Additional file 1 of An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs
Additional file 2 of An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs
Additional file 2 of An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs